Also known as the alpha risk. It’s the acceptable risk of committing a Type A error, or incorrectly rejecting your null hypothesis. Alpha level is always a number between 0 and 1—most commonly, people use a value of 0.05. Once your test is complete and you’ve run the collected data through statistical software, you’ll have a p-value to compare to your alpha level.
A hypothesis that disagrees with the null hypothesis; the two are mutually exclusive.
Also known as the beta risk. It’s the acceptable risk of committing a Type B error – ie, not rejecting your null hypothesis when it is, in fact, incorrect.
A statement which indicates the level of evidence (sufficient or insufficient), at what level of significance, and whether the original claim is rejected (null) or supported (alternative).
Also known as the confidence interval. This refers to how confident you can be that your conclusion is in fact correct. The confidence level is easy to calculate: the alpha and confidence levels always add up to one. ie:
1 – α = confidence level
Set of all values which would cause us to reject the null hypothesis. Also known as a rejection region.
The value(s) which separate the critical region from the non-critical region. The critical values are determined independently of the sample statistics.
A critical value separates the rejection region from the non-rejection region.
A statement based upon the null hypothesis. It is either “reject the null hypothesis” or “fail to reject the null hypothesis”. We will never accept the null hypothesis.
Two basic types of error occur in hypothesis testing: type A errors, where a correct hypothesis is rejected; and type B errors, where an incorrect hypothesis is accepted. Read more about errors.
Also known as the null hypothesis.
Also known as the alternative hypothesis, or H(a).
If the alternative hypothesis H1 contains the less-than inequality symbol (<), the hypothesis test is a left-tailed test.
The statement that you’re trying to disprove. Generally, this is the assumption that the experimental results are due to chance alone; nothing else influenced the results.
A p-value is a crucial element of any hypothesis test results. It’s a number between 0 and 1, and it gauges the probability that random fluctuations caused any data that might cause you to reject the null hypothesis. It’s calculated by running test results through a statistical significance test. If the p-value is lower than your alpha level, then you reject the null hypothesis. If higher, then you do not reject the null hypothesis. Read more about p-values.
Also known as a critical region.
If the alternative hypothesis H1 contains the greater-than inequality symbol (>), the hypothesis test is a right-tailed test.
Also known as the alpha level.
Two Tailed Test
A two-tailed test is one with two rejection regions. If the null hypothesis has an equal sign, then this is a two-tailed test and you can use the test statistic to reject the null hypothesis if the test statistic is too large or too small.
H0: µnew = µcurrent
Ha: µnew ≠ µcurrent